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Journal of Construction Engineering and Management

Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System

Maxwell Fordjour Antwi-Afari, Heng Li, Ph.D., Yantao Yu, Xuejiao Xing

Department of Civil Engineering, College of Engineering and Physical Sciences, Aston University, Birmingham, United Kingdom

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Keywords

Activity recognition, Construction workers, Overexertion risk, Supervised machine learning classifiers, Wearableinsole pressure system, Work-related musculoskeletal disorders

Abstract

Overexertion-related construction activities are identified as a leading cause of work-related musculoskeletal disorders (WMSDs)among construction workers. However, few studies have focused on the automated recognition of overexertion-related construction workers’activities as well as assessing ergonomic risk levels, which may help to minimize WMSDs. Therefore, this study examined the feasibility ofusing acceleration and foot plantar pressure distribution data captured by a wearable insole pressure system for automated recognition ofoverexertion-related construction workers’ activities and for assessing ergonomic risk levels. The proposed approach was tested by simulatingoverexertion-related construction activities in a laboratory setting. The classification accuracy of five types of supervised machine learningclassifiers was evaluated with different window sizes to investigate classification performance and further estimate physical intensity, activityduration, and frequency information. Cross-validation results showed that the Random Forest classifier with a 2.56-s window size achievedthe best classification accuracy of 98.3% and a sensitivity of more than 95.8% for each category of activities using the best features ofcombined data set. Furthermore, the estimation of corresponding ergonomic risk levels was within the same level of risk. The findingsmay help to develop a noninvasive wearable insole pressure system for the continuous monitoring and automated activity recognition, whichcould assist researchers and safety managers in identifying and assessing overexertion-related construction activities for minimizing thedevelopment of WMSDs’ risks among construction workers.

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